Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,777
2	New Jersey	15,937
3	Massachusetts	11,678
4	Rhode Island	11,171
5	Connecticut	9,776
6	District of Columbia	9,329
7	Delaware	7,139
8	Louisiana	7,025
9	Illinois	6,697
10	Maryland	5,777
11	Pennsylvania	4,858
12	Michigan	4,837
13	Nebraska	4,691
14	South Dakota	4,218
15	Iowa	4,211
16	Indiana	3,868
17	Colorado	3,549
18	Mississippi	3,390
19	Georgia	3,189
20	Virginia	3,133
21	Kansas	2,592
22	New Mexico	2,558
23	Washington	2,443
24	New Hampshire	2,426
25	Tennessee	2,381
26	Minnesota	2,290
27	Ohio	2,200
28	Alabama	2,182
29	North Dakota	2,161
30	Nevada	2,107
31	Utah	2,066
32	Florida	1,973
33	Wisconsin	1,872
34	California	1,853
35	Missouri	1,689
36	Arizona	1,672
37	Kentucky	1,589
38	South Carolina	1,559
39	North Carolina	1,511
40	Texas	1,511
41	Vermont	1,488
42	Arkansas	1,403
43	Idaho	1,301
44	Oklahoma	1,226
45	Wyoming	1,188
46	Maine	1,127
47	Oregon	809
48	West Virginia	783
49	Puerto Rico	729
50	Alaska	523
51	Hawaii	442
52	Montana	432

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Illinois	189
2	Rhode Island	176
3	Delaware	172
4	District of Columbia	147
5	Iowa	140
6	Massachusetts	130
7	Nebraska	128
8	Maryland	123
9	Connecticut	121
10	New Jersey	113
11	Virginia	104
12	Minnesota	97
13	New York	88
14	South Dakota	81
15	New Mexico	79
16	Louisiana	76
17	Indiana	69
18	North Dakota	68
19	Mississippi	65
20	Kansas	58
21	Tennessee	58
22	Pennsylvania	56
23	Alabama	55
24	New Hampshire	55
25	Georgia	50
26	Colorado	49
27	Arizona	48
28	Ohio	46
29	Kentucky	45
30	California	43
31	Texas	43
32	Michigan	39
33	Wisconsin	39
34	Utah	38
35	Nevada	36
36	North Carolina	33
37	Washington	30
38	Florida	28
39	Missouri	25
40	Arkansas	24
41	South Carolina	24
42	Oklahoma	22
43	Maine	19
44	Idaho	17
45	Oregon	14
46	Wyoming	14
47	Puerto Rico	13
48	West Virginia	7
49	Alaska	2
50	Hawaii	1
51	Montana	1
52	Vermont	1

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,411
2	New Jersey	1,092
3	Connecticut	876
4	Massachusetts	771
5	Louisiana	497
6	District of Columbia	495
7	Michigan	472
8	Rhode Island	436
9	Pennsylvania	320
10	Illinois	301
11	Maryland	299
12	Delaware	253
13	Indiana	240
14	Colorado	184
15	Mississippi	156
16	Georgia	140
17	Washington	128
18	Ohio	126
19	Minnesota	113
20	New Hampshire	110
21	New Mexico	110
22	Virginia	108
23	Nevada	107
24	Iowa	96
25	Alabama	91
26	Missouri	89
27	Florida	85
28	Vermont	84
29	Arizona	81
30	California	76
31	Kentucky	74
32	Wisconsin	72
33	Oklahoma	70
34	South Carolina	70
35	Kansas	61
36	North Carolina	58
37	Nebraska	55
38	North Dakota	52
39	Maine	49
40	South Dakota	44
41	Texas	41
42	Tennessee	39
43	Idaho	38
44	Puerto Rico	36
45	Arkansas	32
46	West Virginia	32
47	Oregon	31
48	Utah	23
49	Montana	14
50	Alaska	13
51	Hawaii	12
52	Wyoming	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	16
2	New Jersey	16
3	Connecticut	14
4	District of Columbia	12
5	Rhode Island	12
6	New York	11
7	Illinois	10
8	Maryland	9
9	Delaware	7
10	Louisiana	7
11	Pennsylvania	7
12	Colorado	5
13	Indiana	5
14	Michigan	5
15	Iowa	4
16	New Hampshire	4
17	New Mexico	4
18	Ohio	4
19	Alabama	3
20	Georgia	3
21	Minnesota	3
22	Mississippi	3
23	Virginia	3
24	Arizona	2
25	California	2
26	Missouri	2
27	Nevada	2
28	North Dakota	2
29	South Carolina	2
30	Florida	1
31	Kentucky	1
32	Nebraska	1
33	North Carolina	1
34	South Dakota	1
35	Tennessee	1
36	Texas	1
37	Washington	1
38	Wisconsin	1
39	Alaska	0
40	Arkansas	0
41	Hawaii	0
42	Idaho	0
43	Kansas	0
44	Maine	0
45	Montana	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	122,386	1	99
Dakota	Nebraska	72,556	2	99
Lincoln	Arkansas	72,328	3	99
Nobles	Minnesota	59,688	4	99
Lake	Tennessee	42,759	5	99
Davidson	Tennessee	5,588	241	92
Richland	South Carolina	2,807	572	81
Pierce	Washington	1,918	823	73
Orange	California	1,210	1185	62
York	South Carolina	943	1420	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Early	Georgia	2,846	2	99
Terrell	Georgia	2,813	3	99
Essex	New Jersey	1,841	4	99
Nassau	New York	1,819	5	99
Richland	South Carolina	142	476	84
Pierce	Washington	70	800	74
Davidson	Tennessee	55	936	70
Orange	California	25	1268	59
York	South Carolina	18	1395	55

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.